Exploring the Capabilities of CuSum-Near-Real-Time Analysis Using Sentinel-1 Data for Field Monitoring: How Can a Change Detection Method Complement Information from Earth Observations?

This study analyses the potential of a change detection method—the near-real-time cumulative sum change point detection method (CuSum-NRT)—applied to Sentinel-1 C-band synthetic aperture radar (SAR) data to monitor crops and identify field work based on a monthly number of changes. The temporal evolution of the number of changes occurring on Sentinel-1 backscatter at both VV and VH polarisations averaged at field scale was analysed over five years (2017–2021) and compared with NDVI derived from the Sentinel-2 multispectral instrument (MSI) sensor over more than 1000 fields in the southwest of France. The monthly number of changes detected did not show a significant difference between months with soil work and months with no soil work, so further analysis on the dates of changes should be conducted. The number of changes based on VV was found to poorly correlate with the VV backscatter (Rglobal = 0.25), and that based on VH was found to moderately correlate with the VH backscatter (Rglobal = 0.61): CuSum provides additional information compared to backscatter alone. The results also showed that the number of changes detected using CuSum-NRT is correlated to NDVI, mostly positively for the VH polarisation (Rmax = 0.73, p-value < 0.05) and negatively for the VV polarisation (Rmin = −0.69, p-value < 0.005). Furthermore, the analysis of crop groups (cereals, oilseeds, protein crops, fodder, vegetables, others) displayed statistically significant differences in terms of the annual number of changes occurring on both VH and VV polarisations, which has potential applications in crop classification.